Systematic distortions in world Fisheries catch trends

Nature v.414, 29nov01

* Fisheries Centre, 2204 Main Mall, University of
British Columbia, Vancouver, British Columbia V6T 1Z4, Canada

Over 75% of the world marine fisheries catch (over 80
million tonnes per year) is sold on international markets, in contrast to other
food commodities (such as rice)1,2. At present, only one
institution, the Food and Agriculture Organization of the United Nations (FAO)
maintains global fisheries statistics. As an intergovernmental organization,
however, FAO must generally rely on the statistics provided by member countries,
even if it is doubtful that these correspond to reality. Here we show that
misreporting by countries with large fisheries, combined with the large and
widely fluctuating catch of species such as the Peruvian anchoveta, can cause
globally spurious trends. Such trends influence unwise investment decisions by
firms in the fishing sector and by banks, and prevent the effective management
of international fisheries.

World fisheries catches have greatly increased since
1950, when the FAO of the United Nations began reporting global figures3.
The reported catch increases were greatest in the 1960s, when the traditional
fishing grounds of the North Atlantic and North Pacific became fully exploited,
and new fisheries opened at lower latitudes and in the Southern Hemisphere.
Global catches increased more slowly after the 1972 collapse of the Peruvian
anchoveta fishery4, the first fishery collapse that had repercussions
on global supply and prices of fishmeal (Fig. 1a). Even taking into account the
variability of the anchoveta, global catches were therefore widely expected to
plateau in the 1990s at values of around 80 million tonnes, especially as this
figure, combined with estimated discards of 16±40
million tonnes5,
matched the global potential estimates published since the 1960s (ref. 6). Yet the global catches reported by the
FAO generally increased through the 1990s, driven largely by catch reports from
China.

Figure 1
- Time series of global and
Chinese marine fisheries catches (1950 to present). a,
Global reported catch, with and without the highly variable Peruvian anchoveta.
Uncorrected figures are from FAO (ref. 3); corrected values were obtained by
replacing FAO figures by estimates from b. The response to the
1982±83 El Niño/Southern Oscillation (ENSO) is not visible as anchoveta
biomass levels, and hence catches were still very low from the effect of the
previous ENSO in 1972 (ref. 4). b, Reported Chinese catches (from China's exclusive
economic zone (EEZ) and distant water fisheries) increased exponentially from
the mid-1980s to 1998, when the `zero-growth policy' was introduced. The
corrected values for the Chinese EEZ were estimated from the general linear
model described in the Methods section.

These reports appear suspicious for the following three
reasons: (1) The major fish populations along the Chinese coast for which
assessments were available had been classified as overexploited decades ago, and
fishing effort has since continued to climb7,8;
(2) Estimates of catch per unit of effort based on official catch and effort
statistics were constant in the Yellow, East China and South China seas from
1980 to 1995 (ref. 9), that is, during a period of continually increasing
fishing effort and reported catches, and in contrast to declining abundance
estimates based on survey data7; (3) Re-expressing the officially reported catches
from Chinese waters on a per-area basis leads to catches far higher than would
be expected by comparison with similar areas (in terms of latitude, depth,
primary production) in other parts of the world. We investigated the third
reason in some detail by generating world fisheries catch maps on the basis of
FAO fisheries catch statistics for every year since 1950 (see Fig. 2a for a 1998
example). A statistical model was used to describe relationships between
oceanographic and other factors, and the mapped catch. Most high-catch areas of
the world were correctly predicted by the model. These areas typically had very
high primary productivity rates driven by coastal
upwellings, like those off Peru, supporting a large reduction fishery for
the planktivorous anchoveta Engraulis
ringens4. The exception was the waters along the Chinese
coast. Here, the high catches could not be explained by the model using
oceanographic or other factors. Yet the catch statistics provided to FAO by
China have continued to increase from the mid-1980s until 1998 when, under
domestic and international criticism, the government proclaimed a `zero-growth
policy' explicitly stating that reported catches would remain frozen at their
1998 value (Fig. 1b)10.

Mapping the difference between expected (that is,
modelled) catches and those mapped from reported statistics showed large areas
along the Chinese coast that had differences greater than 5 tonnes km-2
year-1.
Overall, the statistical model for 1999 predicted a catch of 5.5 million tonnes,
against an official report of 10.1 million tonnes (see Fig. 1b for earlier
years). Although China was not the only FAO member country reporting relatively
high catches, their large absolute value strongly affects the global total.

For a number of obvious reasons, fishers usually tend to
underreport their catches, and consequently, most countries can be presumed to
under-report their catches to FAO. Thus we wondered why China should differ from
most other countries in this way. We believe that explanation lies in China's
socialist economy, in which the state entities that monitor the economy are also
given the task of increasing its output11.
Until recently, Chinese officials, at all levels, have tended to be promoted on
the basis of production increases from their areas or production units11.
This practice, which originated with the founding of the People's Republic of
China in 1949, became more widespread since the onset of agricultural reforms
that freed the agricultural sector from state directives in the late 1970s (refs
10, 11).

Figure 2 Maps used to correct Chinese
marine fisheries catch in Fig. 1b. a, Map of global catches
reported by FAO for 1998, generated by the rule-based algorithm described in the
Methods section. We note the anomalously high values along the Chinese coast,
comparable in intensity (not area covered) to the extremely productive Peruvian
upwelling system. b, Map of differences in southeast and northeast
Asia between the catches reported in a and
those predicted by the model described in the Methods section.

The Chinese central government appears to be well aware
of this problem, and its 1998 `zero-growth policy' was partly intended to
prevent over-reporting. Thus, the official fisheries catches for 1999± 2000 are
precisely the same as in 1998 (Fig. 1b), and will be for the next few years.
Such measures, although well motivated, do not inspire confidence in official
statistics, past or present.

The substitution of the more realistic estimated series
of Chinese catches into the FAO fisheries statistics led to global catch
estimates which, although fluctuating, have tended to decline by 0.36 million
tonnes year-1 since 1988 (rather than increase by 0.33 million
tonnes year-1, as suggested by the uncorrected data). The global
downward trend becomes clearer when the catches of a single species, the
Peruvian anchoveta, which is known to be affected by El Niño/Southern
Oscillation events, is subtracted (see Fig. 1a). In this case, a significant (P,
0:01),
and so far undocumented downward trend of 0.66 million tonnes year-1
becomes
apparent for all other species and fisheries. This is consistent with other
accounts of worldwide declines of fisheries12,13.

Ironically, it is likely that, at the lowest levels
(individual fishers), catches are under-reported in China as elsewhere in the
world. The production targets caused these reports to be exaggerated. At some
times these two distortions may perhaps have cancelled each other out, and an
accurate report of catches may have been submitted to FAO. Since the early
1990s, however, the exaggerations have apparently far exceeded any initial under-reporting.

The greatest impact of inflated global catch statistics
is the complacency that it engenders. There seems little need for public
concern, or intervention by
international agencies, if the world's fisheries are keeping pace with people's
needs. If, however, as the adjusted figures demonstrate, the catches of world
fisheries are in general decline, then there is a clear need to act. The oceans
should continue to provide for a substantial portion of the world's protein
needs. The present trends of overfishing, wide-scale disruption of coastal
habitats and the rapid expansion of non-sustainable aquaculture enterprises14,
however, threaten the world's food security.

Methods

Data processing involved a disaggregation of global
fisheries catch statistics firstly into detailed taxonomic groups, and then into
fine-scale spatial cells (a half-degree of latitude by a half-degree of
longitude), using a variety of databases and systematic rules15.
The spatially disaggregated catches provided the basis for a general linear
model of fisheries catches (see below). The model predicted the likely catches
in the spatial cells in the Chinese exclusive economic zone (EEZ), thus
providing an estimate of Chinese catches (including Hong Kong and Macau, but
excluding Taiwan).

Data sources

Fisheries catch statistics were provided by the FAO
(FishStat3 and `Atlas of Tuna and Billfish Catches', http://www.fao.org/fi/atlas/tunabill/english/home/htm.
The spatial cells were described by depth (US National Geophysical Data Center),
primary productivity (Joint Research Centre of the European Commission Space
Applications Institute– Marine Environment Unit, http://www.gmes.jrc.it/download/kyoto_prot/glob.marine.pdf,
biogeochemical provinces16, the presence of ice (US National Snow and Ice
Data Center, http://www.nsidc.org, surface temperature (NOAA's Marine Atlas,
http://www.nodc.noaa.gov/OC5/data_woa.html, and an upwelling index calculated
for each cell by multiplying negative deviations in surface temperature (from the
average for that latitude and ocean) by the primary productivity in that cell.
Fishing access rights were determined using maps of the exclusive economic zones
(EEZ) of coastal states17 and a database of fishing access agreements18.

Taxonomic disaggregation

The fisheries statistics of several nations commonly
include a large fraction of catches in `miscellaneous' categories. Chinese
catches so reported were disaggregated on the basis of the breakdown provided by
its two nearest maritime neighbours with detailed marine fisheries statistics
(Taiwan and South Korea)15.
Assigning catches to lower taxa allowed the use of biological information in the
spatial disaggregation process.

Spatial disaggregation

A database of the global distribution of commercial
fisheries species was developed using information from a variety of sources
including the FAO, FishBase19 and experts on various resource
species or groups. Some distributions were specific; others provided depth or
latitudinal limits, or simple presence/absence data. The spatial disaggregation
process determined the intersection set of spatial cells within the broad
statistical area for which the statistics were provided to FAO, the global
distribution of the reported species, and the cells to which the reporting
nation had access through fishing agreements15. The reported catch tonnage was then proportioned
within this set of cells.

Catch predictions

A general linear model was developed in the software
package S-Plus20. The model relates log fisheries catch (in tonnes
km-2 year-1)
for each cell (the dependent variable) to depth, primary productivity, ice
cover, surface temperature, latitude, distance from shore, upwelling index (the
continuous predictor variables), 33 oceanic biogeochemical provinces and one
global coastal `biome'16 including most of the area
covered by the world's EEZs, including China's (the categorical predictor
variables). Fishing effort was not used in the prediction and catches were
assumed to be generally close to their maximum biologically sustainable limits.
The additive and variance stabilizing transformation (AVAS) routine of S-Plus20
was
used to identify transformations ensuring linearity between the dependent and
explanatory variables, and the model was then used to predict the catch from
each spatial cell. Those from Chinese waters were combined, then compared with
the catches obtained from the rule-based spatial disaggregation described above.

Trend analyses

The estimates of recent trends of global catch were
estimated by linear regression of catch versus year, for the period from 1988
(highest catches, anchoveta excluded) to 1999 (last year with FAO data), for
uncorrected global marine catches, global marine catches adjusted for Chinese
over-reporting, and adjusted catches minus the catch of Peruvian anchoveta.

We thank V. Christensen for the upwelling index, and A.
Gelchu for the species distribution shape files used here. We also thank our
colleagues in the `Sea Around Us' Project. This work was supported by the Pew
Charitable Trusts through the `Sea Around Us' Project, Fisheries Centre,
University of British Columbia. D.P. also acknowledges support from the National
Science and Engineering Council of Canada. Correspondence and requests for
materials should be addressed to R.W. (e-mail: r.watson@fisheries.ubc.ca
).

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